Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model

Abstract

A novel bioinspired metaheuristic is proposed in this work, simulating how the Coronavirus spreads and infects healthy people. From an initial individual (the patient zero), the coronavirus infects new patients at known rates, creating new populations of infected people. Every individual can either die or infect and, afterwards, be sent to the recovered population. Relevant terms such as re-infection probability, super-spreading rate or traveling rate are introduced in the model in order to simulate as accurately as possible the coronavirus activity. The Coronavirus Optimization Algorithm has two major advantages compared to other similar strategies. First, the input parameters are already set according to the disease statistics,

A Survey on Evolutionary Neural Architecture Search

“…Evans et al [149] integrate the local search (as gradient descent) into the GP as a fine-tuning operation. The CVOA [85] is inspired by the new respiratory virus, COVID-19. The architecture is found by simulating the virus spreads and infects healthy individuals.…”